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A living "brain" of cultured rat cells now controls an F-22 fighter jet flight simulator, a U.S. scientist says.

Scientists say the research could lead to tiny, brain-controlled prosthetic devices and living computers flying pilotless aeroplanes.

And if scientists can decipher the ground rules of how such neural networks function, the research may also result in novel computing systems to tackle dangerous search-and-rescue jobs and assess bomb damage without endangering humans.

Interaction of the cells within the lab-assembled brain may also allow scientists to better understand how the human brain works. The data may one day enable researchers to determine causes and possible non-invasive cures for neural disorders, such as epilepsy.

For the recent project, Thomas DeMarse, a University of Florida professor of biomedical engineering, placed an electrode grid at the bottom of a glass dish and then covered the grid with rat neurones.

The cells initially resembled individual grains of sand in liquid. But they soon extended microscopic lines toward each other, gradually forming a neural network, a brain, that DeMarse said was a "living computational device".

The brain then communicated with the flight simulator through a desktop computer.

"We grow approximately 25,000 cells on a 60-channel multi-electrode array, which permits us to measure the signals produced by the activity of each neurone as it transmits information across this network of living neurones," DeMarse said.

"Using these same channels [electrodes] we can also stimulate activity at each of the 60 locations [electrodes] in the network. Together, we have a bidirectional interface to the neural network where we can input information via stimulation. The network processes the information, and we can listen to the network's response."

Can neural networks learn?

The brain can learn, just as a human brain learns, he said. When the system is first engaged, the neurones don't know how to control the aeroplane; they don't have any experience.

"[But] over time, these stimulations modify the network's response such that the neurones slowly [over the course of 15 minutes] learn to control the aircraft," he said.

"The end result is a neural network that can fly the plane to produce relatively stable straight and level flight."

At present, the brain can control the pitch and roll of the F-22 in various virtual weather conditions, ranging from hurricane-force winds to clear blue skies.

This brain-controlled plane may sound like science fiction, but it is grounded in work that has been taking place for more than a decade.

A breakthrough occurred in 1993, when a team of scientists created a Hybrot, short for "hybrid robot".

The robot consisted of hardware, computer software, rat neurones, and incubators for those neurones.

The computer, programmed to respond to the neurone impulses, controlled a wheel underneath a machine that resembled a child's toy robot.

Last year, U.S. and Australian researchers used a similar neurone-controlled robotic device to produce a "semi-living artist".

In this case, the neurones were hooked up to a drawing arm outfitted with different coloured markers.

The robot drew decipherable pictures, albeit it bad ones that resembled child scribbles, but that technology led to today's fighter plane simulator success.

What can we learn from neural networks?

Steven Potter, an assistant professor of biomedical engineering at Georgia Tech who directed the living artist project, believed DeMarse's work was important, and that such studies could lead to a variety of engineering and neurobiology research goals.

"A lot of people have been interested in what changes in the brains of animals and people when they are learning things," Potter said.

"We're interested in getting down into the network and cellular mechanisms, which is hard to do in living animals. And the engineering goal would be to get ideas from this system about how brains compute and process information."

Though the "brain" can successfully control a flight simulation program, more elaborate applications are a long way off, DeMarse said.

"We're just starting out. But using this model will help us understand the crucial bit of information between inputs and the stuff that comes out," he said. "And you can imagine the more you learn about that, the more you can harness the computation of these neurones into a wide range of applications."